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Trust-based fusion of classifiers for static code analysis

Title	Trust-based fusion of classifiers for static code analysis
Author	Yüksel, U., Sözer, Hasan, Şensoy, Murat
Publication Date:	2014
Publication Place	- IEEE
Subject	Classifer fusion, Trust-based fusion, Alert classification, Industrial case study, Static code analysis
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-84910594174
Record ID	da8fa851-6ea7-4a78-bccf-c998a9b71cd2
Library Location	Computer Science
Date	2014
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. The effectiveness of many different classifiers and artifact characteristics have been evaluated for this application domain. However, the effectiveness of classifier fusion methods have not been investigated yet. In this work, we evaluate several existing classifier fusion approaches in the context of an industrial case study to classify the alerts generated for a digital TV software. In addition, we employ a trust-based classifier fusion method. We observed that our approach can increase the accuracy of classification by up to 4%.

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Özyeğin University

Trust-based fusion of classifiers for static code analysis

Author Yüksel, U., Sözer, Hasan, Şensoy, Murat

Publication Date 2014

Publication Place - IEEE

Subject Classifer fusion, Trust-based fusion, Alert classification, Industrial case study, Static code analysis

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-84910594174

Record ID da8fa851-6ea7-4a78-bccf-c998a9b71cd2

Library Location Computer Science

Date 2014

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text Static code analysis tools automatically generate alerts for potential software faults that can lead to failures. However, developers are usually exposed to a large number of alerts. Moreover, some of these alerts are subject to false positives and there is a lack of resources to inspect all the alerts manually. To address this problem, numerous approaches have been proposed for automatically ranking or classifying the alerts based on their likelihood of reporting a critical fault. One of the promising approaches is the application of machine learning techniques to classify alerts based on a set of artifact characteristics. The effectiveness of many different classifiers and artifact characteristics have been evaluated for this application domain. However, the effectiveness of classifier fusion methods have not been investigated yet. In this work, we evaluate several existing classifier fusion approaches in the context of an industrial case study to classify the alerts generated for a digital TV software. In addition, we employ a trust-based classifier fusion method. We observed that our approach can increase the accuracy of classification by up to 4%.